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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 "cells": [
  {
   "source": [
    "案例：tianmao"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 分析背景\n",
    "“天猫”（英文：Tmail，亦称淘宝商城，天猫商城）原名淘宝商城，是一个综合性购物网站，也是马云淘宝网打造的B2C(Business-to-Consumer, 商业零售)品牌。其整合数千家品牌商、生产商，为商家和消费者之间提供一站式解决方案，提供100%品质保证的商品，7天无理由退货的售后服务，以及购物积分返现等优质服务"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 分析目标\n",
    "根据用户数据以及消费行为数据\n",
    "- 使用Python建立分类模型，进行逻辑回归\n",
    "- 预测使用优惠券概率较高的客群"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# 1 数据概况分析"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1.1 数据预览"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 指标解释\n",
    "- ID\t记录编码\n",
    "\n",
    "- age\t年龄\n",
    "\n",
    "- job\t职业\n",
    "\n",
    "- marital\t婚姻状态\n",
    "\n",
    "- default\t花呗是否有违约\n",
    "\n",
    "- returned\t是否有过退货\n",
    "\n",
    "- loan\t是否使用花呗结账\n",
    "\n",
    "- coupon_used_in_last6_month\t过去六个月使用的优惠券数量\n",
    "\n",
    "- coupon_used_in_last_month\t过去一个月使用的优惠券数量\n",
    "\n",
    "- coupon_ind\t该次活动中是否有使用优惠券"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   ID  age         job   marital default returned loan  \\\n0   1   43  management   married      no      yes   no   \n1   2   42  technician  divorced      no      yes   no   \n2   3   47      admin.   married      no      yes  yes   \n3   4   28  management    single      no      yes  yes   \n4   5   42  technician  divorced      no      yes   no   \n\n   coupon_used_in_last6_month  coupon_used_in_last_month  coupon_ind  \n0                           2                          0           0  \n1                           1                          1           0  \n2                           2                          0           0  \n3                           2                          0           0  \n4                           5                          0           0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>default</th>\n      <th>returned</th>\n      <th>loan</th>\n      <th>coupon_used_in_last6_month</th>\n      <th>coupon_used_in_last_month</th>\n      <th>coupon_ind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>43</td>\n      <td>management</td>\n      <td>married</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>42</td>\n      <td>technician</td>\n      <td>divorced</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>47</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>28</td>\n      <td>management</td>\n      <td>single</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>42</td>\n      <td>technician</td>\n      <td>divorced</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "#导入模块和数据\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "coupon = pd.read_csv('tianmao.csv')\n",
    "coupon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(25317, 10)"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "#查看数据行列总数\n",
    "coupon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 25317 entries, 0 to 25316\nData columns (total 10 columns):\n #   Column                      Non-Null Count  Dtype \n---  ------                      --------------  ----- \n 0   ID                          25317 non-null  int64 \n 1   age                         25317 non-null  int64 \n 2   job                         25317 non-null  object\n 3   marital                     25317 non-null  object\n 4   default                     25317 non-null  object\n 5   returned                    25317 non-null  object\n 6   loan                        25317 non-null  object\n 7   coupon_used_in_last6_month  25317 non-null  int64 \n 8   coupon_used_in_last_month   25317 non-null  int64 \n 9   coupon_ind                  25317 non-null  int64 \ndtypes: int64(5), object(5)\nmemory usage: 1.9+ MB\n"
    }
   ],
   "source": [
    "#查看数据是否有缺失值\n",
    "coupon.info()"
   ]
  },
  {
   "source": [
    "查看完数据整体后，发现无缺失值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 1.2 数据清洗"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 1.2.1 类别型变量"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   default_no  default_yes  returned_no  returned_yes  loan_no  loan_yes\n0           1            0            0             1        1         0\n1           1            0            0             1        1         0\n2           1            0            0             1        0         1\n3           1            0            0             1        0         1\n4           1            0            0             1        1         0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>default_no</th>\n      <th>default_yes</th>\n      <th>returned_no</th>\n      <th>returned_yes</th>\n      <th>loan_no</th>\n      <th>loan_yes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "#将类别型变量转换为数字型变量，便于之后分析\n",
    "#但在本案例中，为了后续分析方便，只处理default, returned, loan这三个变量，保留job, marital \n",
    "#把default、returned、loan三个变量单独取出来进行哑变量处理get_dummies()。\n",
    "coupon1 = coupon[['default','returned','loan']]\n",
    "coupon1 = pd.get_dummies(coupon1)\n",
    "coupon1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   ID  age         job   marital default returned loan  \\\n0   1   43  management   married      no      yes   no   \n1   2   42  technician  divorced      no      yes   no   \n2   3   47      admin.   married      no      yes  yes   \n3   4   28  management    single      no      yes  yes   \n4   5   42  technician  divorced      no      yes   no   \n\n   coupon_used_in_last6_month  coupon_used_in_last_month  coupon_ind  \\\n0                           2                          0           0   \n1                           1                          1           0   \n2                           2                          0           0   \n3                           2                          0           0   \n4                           5                          0           0   \n\n   default_no  default_yes  returned_no  returned_yes  loan_no  loan_yes  \n0           1            0            0             1        1         0  \n1           1            0            0             1        1         0  \n2           1            0            0             1        0         1  \n3           1            0            0             1        0         1  \n4           1            0            0             1        1         0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>default</th>\n      <th>returned</th>\n      <th>loan</th>\n      <th>coupon_used_in_last6_month</th>\n      <th>coupon_used_in_last_month</th>\n      <th>coupon_ind</th>\n      <th>default_no</th>\n      <th>default_yes</th>\n      <th>returned_no</th>\n      <th>returned_yes</th>\n      <th>loan_no</th>\n      <th>loan_yes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>43</td>\n      <td>management</td>\n      <td>married</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>42</td>\n      <td>technician</td>\n      <td>divorced</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>47</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>28</td>\n      <td>management</td>\n      <td>single</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>42</td>\n      <td>technician</td>\n      <td>divorced</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "#把处理后的表格和原表格进行拼接\n",
    "coupon = pd.concat([coupon, coupon1], axis = 1)\n",
    "coupon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 25317 entries, 0 to 25316\nData columns (total 9 columns):\n #   Column                      Non-Null Count  Dtype \n---  ------                      --------------  ----- \n 0   age                         25317 non-null  int64 \n 1   job                         25317 non-null  object\n 2   marital                     25317 non-null  object\n 3   coupon_used_in_last6_month  25317 non-null  int64 \n 4   coupon_used_in_last_month   25317 non-null  int64 \n 5   flag                        25317 non-null  int64 \n 6   default_yes                 25317 non-null  uint8 \n 7   returned_yes                25317 non-null  uint8 \n 8   loan_yes                    25317 non-null  uint8 \ndtypes: int64(4), object(2), uint8(3)\nmemory usage: 1.2+ MB\n"
    }
   ],
   "source": [
    "#删除包含重复信息和无意义信息的数据\n",
    "coupon.drop(['ID', 'default', 'default_no', 'returned', 'returned_no', 'loan', 'loan_no'], axis = 1, inplace = True)\n",
    "\n",
    "#将coupon_ind重新命名为flag，便于之后分析\n",
    "coupon = coupon.rename(columns = {'coupon_ind' : 'flag'})\n",
    "coupon.info()"
   ]
  },
  {
   "source": [
    "# 2 单变量分析"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 2.1 观察样本0、1的平衡性"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.883043\n1    0.116957\nName: flag, dtype: float64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "#二分类模型，观察样本(flag)0,1的平衡性\n",
    "coupon['flag'].value_counts(1)"
   ]
  },
  {
   "source": [
    "- 在二分类问题中，0、1的占比要保持平衡型，实际情况中不低于0.05，否则会影响模型的预测\n",
    "- 该数据集0、1占比均高于0.05，因此其分布是合理的"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 2.2 观察均值大小"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            age  coupon_used_in_last6_month  coupon_used_in_last_month  \\\nflag                                                                     \n0     40.819601                    2.857846                   0.260378   \n1     41.809524                    2.124282                   0.537994   \n\n      default_yes  returned_yes  loan_yes  \nflag                                       \n0        0.018876      0.579755  0.169037  \n1        0.008781      0.357649  0.094563  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>coupon_used_in_last6_month</th>\n      <th>coupon_used_in_last_month</th>\n      <th>default_yes</th>\n      <th>returned_yes</th>\n      <th>loan_yes</th>\n    </tr>\n    <tr>\n      <th>flag</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40.819601</td>\n      <td>2.857846</td>\n      <td>0.260378</td>\n      <td>0.018876</td>\n      <td>0.579755</td>\n      <td>0.169037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>41.809524</td>\n      <td>2.124282</td>\n      <td>0.537994</td>\n      <td>0.008781</td>\n      <td>0.357649</td>\n      <td>0.094563</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "#先按客户是否使用coupon进行分类聚合\n",
    "summary = coupon.groupby(['flag'])\n",
    "#求出各种情况均值的占比情况\n",
    "summary.mean()"
   ]
  },
  {
   "source": [
    "### 对于数据类型为0和1的变量，观察均值大小可以帮助我们分析这个变量在flag上的分布：\n",
    "- coupon_used_in_last_month在0的均值为0.26，在1的均值为0.53，说明越是在上个月使用了coupon的客户，接下来再使用coupon的概率会越高\n",
    "- default_yes和loan_yes在0时的均值均大于在1时的均值，说明花呗违约和用花呗结账的客户在接下来的时间里使用coupon的概率较小\n",
    "- age在0和1的均值分别为40.8和41.8，差别不大，说明年龄无太大的区分关系"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 2.3 可视化"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10dc7b350>"
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#观察returned_yes在flag上的分布\n",
    "sns.countplot(y = 'returned_yes', hue = 'flag', data = coupon)"
   ]
  },
  {
   "source": [
    "- 相比起没有退货的客户，退货的客户使用coupon的概率较小"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#观察marital在flag上的分布\n",
    "sns.countplot(y = 'marital', hue = 'flag', data = coupon)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a26e48710>"
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "source": [
    "- 已婚客户使用coupon的概率比未婚和离婚客户使用coupon的概率略高\n",
    "- 已婚人士未使用coupon的概率比未婚人士未使用coupon的概率也要高\n",
    "- 但是三者均未使用coupon的概率比使用coupon的概率要高得多"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a2787d250>"
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#观察job在flag上的分布\n",
    "sns.countplot(y = 'job', hue = 'flag', data = coupon, order = coupon['job'].value_counts().index)"
   ]
  },
  {
   "source": [
    "- job title为management, technician, blue-collar的客户越有可能使用coupon"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a27196a50>"
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#观察age在flag上的分布\n",
    "sns.distplot(coupon['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count    25317.000000\nmean        40.935379\nstd         10.634289\nmin         18.000000\n25%         33.000000\n50%         39.000000\n75%         48.000000\nmax         95.000000\nName: age, dtype: float64"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "#查看age在整体数据的分布情况\n",
    "coupon['age'].describe()"
   ]
  },
  {
   "source": [
    "- 数据显示18 - 95岁的客户使用coupon可能性较高，使用coupon概率较高的客群集中在40岁\n",
    "- 发现age > 60岁的极端值较少，但它们影响了整体数据分布，需要把这部分数据剔除在分析范围外"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "           count       mean       std   min   25%   50%   75%   max\nage_new                                                            \n<20         25.0  18.760000  0.435890  18.0  19.0  19.0  19.0  19.0\n<40      13063.0  32.569624  4.130196  20.0  30.0  33.0  36.0  39.0\n<60      11215.0  48.416942  5.720111  40.0  43.0  48.0  53.0  59.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>age_new</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>&lt;20</th>\n      <td>25.0</td>\n      <td>18.760000</td>\n      <td>0.435890</td>\n      <td>18.0</td>\n      <td>19.0</td>\n      <td>19.0</td>\n      <td>19.0</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>&lt;40</th>\n      <td>13063.0</td>\n      <td>32.569624</td>\n      <td>4.130196</td>\n      <td>20.0</td>\n      <td>30.0</td>\n      <td>33.0</td>\n      <td>36.0</td>\n      <td>39.0</td>\n    </tr>\n    <tr>\n      <th>&lt;60</th>\n      <td>11215.0</td>\n      <td>48.416942</td>\n      <td>5.720111</td>\n      <td>40.0</td>\n      <td>43.0</td>\n      <td>48.0</td>\n      <td>53.0</td>\n      <td>59.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "#对于年龄进行快速分组，探究各个年龄段对于是否使用coupon的影响\n",
    "age60 = coupon[coupon['age'] < 60]\n",
    "bins = [0, 20, 40, 60]\n",
    "labels = ['<20','<40','<60']\n",
    "age60['age_new'] = pd.cut(age60.age, bins, right=False,labels = labels)\n",
    "age60.groupby(['age_new'])['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count     24303\nunique        3\ntop         <40\nfreq      13063\nName: age_new, dtype: object"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "age60['age_new'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "#绘制age60['age_new']的饼图，使数据更加直观\n",
    "plt.figure(figsize=[9,7])\n",
    "age60['age_new'].value_counts().plot.pie()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "- 20 - 40岁的客群使用coupon概率最高\n",
    "- 18，32，48岁分别为三个年龄段中使用coupon概率较高的年龄平均值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# 3 相关和可视化"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                flag\nflag                        1.000000\ncoupon_used_in_last_month   0.116550\nage                         0.029916\ndefault_yes                -0.024608\nloan_yes                   -0.065231\ncoupon_used_in_last6_month -0.075173\nreturned_yes               -0.143589",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>flag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>flag</th>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>coupon_used_in_last_month</th>\n      <td>0.116550</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>0.029916</td>\n    </tr>\n    <tr>\n      <th>default_yes</th>\n      <td>-0.024608</td>\n    </tr>\n    <tr>\n      <th>loan_yes</th>\n      <td>-0.065231</td>\n    </tr>\n    <tr>\n      <th>coupon_used_in_last6_month</th>\n      <td>-0.075173</td>\n    </tr>\n    <tr>\n      <th>returned_yes</th>\n      <td>-0.143589</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "#围绕flag变量，观察它与其他变量的关系\n",
    "coupon.corr()[['flag']].sort_values('flag', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a29f24390>"
     },
     "metadata": {},
     "execution_count": 72
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(coupon.corr(), cmap = 'Blues')"
   ]
  },
  {
   "source": [
    "- flag与coupon_used_in_last_month, age成强正相关关系\n",
    "- flag与coupon_used_in_last6_month, returned_yes成强负相关关系\n",
    "- 其他变量与flag的相关不明显，为了分析准确性，因此不做过度解读"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# 4 逻辑回归模型的建立和评估"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 4.1 模型建立\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 4.1.1 抽取训练集和测试集并进行拟合"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先设定自变量x和因变量y\n",
    "x = coupon[['coupon_used_in_last_month', 'returned_yes', 'loan_yes']]\n",
    "y = coupon['flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"
     },
     "metadata": {},
     "execution_count": 121
    }
   ],
   "source": [
    "#调用sklearn模块，随机抽取训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 100) #训练集和测试集抽取比例为70/30\n",
    "\n",
    "#调用sklearn中逻辑回归模块\n",
    "from sklearn import linear_model\n",
    "lr = linear_model.LogisticRegression()\n",
    "\n",
    "#拟合\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "source": [
    "### 4.1.2 查看模型结果"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[ 0.38674154, -0.95906584, -0.56226592]])"
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "source": [
    "#查看系数\n",
    "lr.coef_"
   ]
  },
  {
   "source": [
    "- 当coupon_used_in_last_month从0到1时，不使用coupon到使用coupon的概率提升是e的0.38，即是其他组客户的1.46倍\n",
    "- 当returned_yes从1到0时，使用coupon到不使用coupon的概率提升是其他组客户的0.38倍\n",
    "- 当loan_yes从1到0时，使用coupon到不使用coupon的概率提升是其他组客户的0.57倍\n",
    "- 故从概率来看，上月使用过coupon的客户、未退货客户和未用花呗付款的客群再使用coupon的概率会更高。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([-1.63641623])"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "#查看截距\n",
    "lr.intercept_"
   ]
  },
  {
   "source": [
    "## 4.2 模型评估"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 4.2.1 评估方法一：计算准确度"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#通过训练集和测试集的自变量x，分别计算出对应的预测值\n",
    "y_pred_train = lr.predict(x_train)\n",
    "y_pred_test = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[15596,    27],\n       [ 2092,     6]])"
     },
     "metadata": {},
     "execution_count": 88
    }
   ],
   "source": [
    "#搭建训练集混淆矩阵\n",
    "from sklearn import metrics\n",
    "metrics.confusion_matrix(y_train, y_pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.8804243552846904"
     },
     "metadata": {},
     "execution_count": 89
    }
   ],
   "source": [
    "#查看训练集准确率\n",
    "metrics.accuracy_score(y_train, y_pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[6722,   11],\n       [ 862,    1]])"
     },
     "metadata": {},
     "execution_count": 90
    }
   ],
   "source": [
    "#搭建测试集混淆矩阵\n",
    "metrics.confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.8850710900473934"
     },
     "metadata": {},
     "execution_count": 91
    }
   ],
   "source": [
    "#查看测试集准确率\n",
    "metrics.accuracy_score(y_test, y_pred_test)"
   ]
  },
  {
   "source": [
    "#### 比较训练集和测试集的准确率，保证内在信息一致性:\n",
    "- 模型在train和test的表现中不能相差过大。案例中训练集与测试集的准确率相差很小，故该模型是合理的\n",
    "- 但是未来客群和特点都可能会发生变化，需要做出自己的平衡。train和test只是帮助建立一个概念，不要为了单独的模型太过追求准确率"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 4.2.2 评估方法二：ROC和AUC"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.5005658226636232\n"
    }
   ],
   "source": [
    "#使用auc评估模型\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr,tpr,threshold = roc_curve(y_train, y_pred_train)\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "print(roc_auc)"
   ]
  },
  {
   "source": [
    "一般好的模型评分在0.7 - 0.8之间，故此模型需要被调整"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 4.3 模型优化"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#新增变量returned_yes\n",
    "x = coupon[['coupon_used_in_last_month', 'returned_yes', 'loan_yes', 'coupon_used_in_last6_month', 'default_yes', 'age']]\n",
    "y = coupon['flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"
     },
     "metadata": {},
     "execution_count": 137
    }
   ],
   "source": [
    "#调用sklearn模块，随机抽取训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 100) #训练集和测试集抽取比例为70/30\n",
    "\n",
    "#调用sklearn中逻辑回归模块\n",
    "from sklearn import linear_model\n",
    "lr = linear_model.LogisticRegression()\n",
    "\n",
    "#拟合\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[ 0.43301838, -0.98377924, -0.53258362, -0.16774917, -0.53712933,\n         0.00114054]])"
     },
     "metadata": {},
     "execution_count": 138
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.5005658226636232\n"
    }
   ],
   "source": [
    "#使用auc评估模型\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr,tpr,threshold = roc_curve(y_train, y_pred_train)\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "print(roc_auc)"
   ]
  },
  {
   "source": [
    "- 只有coupon_used_in_last_month, age和flag的成正相关关系，其他变量与flag均为反相关关系\n",
    "- 模型迭代后AUC评分变化不大，说明coupon的使用率明显较低"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# 5 业务建议"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 5.1 用户分析"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "- 数据显示18 - 95岁的客户使用coupon可能性较高，使用coupon概率较高的客群集中在40岁\n",
    "- 20 - 40岁的客群使用coupon概率最高\n",
    "- 18，32，48岁分别为三个年龄段中使用coupon概率较高的年龄平均值"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 5.2 提高优惠券使用率分析 - 高价值用户"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "- coupon_used_in_last_month在0的均值为0.26，在1的均值为0.53，说明越是在上个月使用了coupon的客户，接下来再使用coupon的概率会越高\n",
    "- default_yes和loan_yes在0时的均值均大于在1时的均值，说明花呗违约和用花呗结账的客户在接下来的时间里使用coupon的概率较小\n",
    "\n",
    "\n",
    "- 相比起没有退货的客户，退货的客户使用coupon的概率较小\n",
    "- 已婚客户使用coupon的概率比未婚和离婚客户使用coupon的概率略高\n",
    "- job title为management, technician, blue-collar的客户越有可能使用coupon\n",
    "\n",
    "\n",
    "- flag与coupon_used_in_last_month, age成强正相关关系\n",
    "- flag与coupon_used_in_last6_month, returned_yes成强负相关关系\n",
    "- 其他变量与flag的相关性都不明显"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 5.3 结论"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "- 天猫优惠券的使用率较低\n",
    "- 重点留意20 - 60客户的留存情况，对有购买潜力或者购买种类比较单一的客户，制定向上销售模型或交叉销售模型，提升现有客户价值\n",
    "- 鼓励上月用过优惠券的客户再次使用优惠券，制定相应的产品响应模型或活动响应模型，最大化收益\n",
    "- 对无退货客户、已婚客户、无花呗违约和未用花呗结账的用户制定客户流失预警模型或者客户赢回模型，其次是管理、技术员和蓝领阶层，尽量挽回客户\n",
    "- 加大APP内优惠券的宣传力度，加强banner、广告推送等营销措施；在APP外进行额外推送、第三方优惠券推送，让客户了解并提升使用优惠券的概率"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}